Field of the Invention
Embodiments of the present invention are generally related to methods and apparatus for predicting weather. More specifically, embodiments of the present invention relate apparatus and methods of point prediction; extending the prediction lead time using predictive distributions; inversion of patterns in real world data to determine parameters of the dynamic model; and/or the like.
Description of Related Art
Predicting variations in weather conditions, climate conditions, or chaotic systems may have many practical applications. For example, predicting variations in precipitation at a local and regional level is important to a countless number of commercial, industrial and recreational activities, ranging from agriculture, to allocation of emergency service resources, to an ideal location for a little league baseball field. Prediction of regional interseasonal to interannual precipitation, however, has been described as one of the major challenges to climate science. Part of the challenge arises from the chaotic nature of the climate system, and part of it from the non-stationarity due to the rapid rise in global surface air temperature.
Weather prediction techniques have improved greatly in recent years. As weather predictions have become more accurate, businesses have incorporated weather-related analysis into their corporate planning decisions. Information concerning tornadoes, hurricanes, severe thunderstorms and the like have been used by utility companies, manufacturing plants, airlines, and other businesses to avoid losses. There are many limitations, however, to known methods of predicting precipitation and thus, a new method and apparatus is needed. Particular limitations that this method aims at, are poor skill even for low lead times (e.g. ½ month) for predicting seasonal precipitation (a climate prediction), and poor skill in daily (weather) precipitation predicting beyond 7 days. The methods described here build on methods described in a prior application WO2012/047874 A2, which is incorporated by reference as if fully set forth herein.
Neither purely empirical regression, nor General or Global Circulation Models (GCMs) are sufficiently accurate for interseasonal to interannual prediction of precipitation. A general circulation model (GCM), a type of climate model, is a mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier-Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). General or Global Circulation Models (GCMs) are not accurate due to both uncertainty in initial conditions, and lack of sufficient detail on the integration of small scale physics. Statistical time series methods such as regressions can work temporarily, but do not reflect non-stationarity in the climate system, in particular the rapid rise in global surface air temperature (GSAT). Thus, there is a need for an improved method and apparatus for prediction of weather, climate, chaotic systems, or the like. For example, there is a need for an improved method and apparatus for prediction of regional interseasonal to interannual precipitation.